Part 4.1: Rough annotation

Please see this part for how to apply the annotation to the clustered data set and how to create the plots. This part (4.1) should be included in the report.

Read packages and data

library(Seurat)
## Attaching SeuratObject
library(clustree)
## Loading required package: ggraph
## Loading required package: ggplot2
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)

Clean environment

rm(list = ls())
gc()
##           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
## Ncells 3081136 164.6    5002700 267.2         NA  5002700 267.2
## Vcells 5280843  40.3   10146329  77.5      16384  8187290  62.5

Load Data

ple5 <- readRDS("srt_03_ple5v2.rds")
ple5.markers<-readRDS("markers_res_0.5v2.rds")

Plot Clusters

DimPlot(ple5, group.by="RNA_snn_res.0.5", label=TRUE)

Annotating cell types

Each cell will be assigned a broad cell type based on the gene expression profiles.

Assign broad cell names to glutamatergic cells

Our data come from the telencephalon of the salamander Pleurodeles waltl as published in Woych et al., 2022. To annotate the clusters information from the aforementioned publication, as well a marker gene database of the mouse brain (http://mousebrain.org/adolescent/celltypes.html) were used. It should be kept in mind though, that amphibians and mammals are very distantly related and there are significant differences when it comes to cell types and gene expression profiles.

Sort markers by p_val_adj for each cluster.

for (i in 0:18) {
  current_cluster_markers <- ple5.markers %>%
    dplyr::filter(cluster == i) %>%
    dplyr::arrange(p_val_adj)
  
  assign(paste0("cluster", i, "_markers"), current_cluster_markers)
}

According to the original publication “Telencephalic GABAergic neurons express markers of the subpallium, such as Dlx5, Gad1, and Gad2.”

ple5 <- SetIdent(ple5, value="RNA_snn_res.0.5")
FeaturePlot(ple5, features=c("DLX5", "GAD1", "GAD2"), label=TRUE, pt.size = 0.1)

It appears that clusters 0, 4, 5, 6, 7, 10, 14 and 15 are inhibitory GABAergic neurons.

In the publication SLC17A7 is the marker gene for telencephalic glutamatergic neurons (TEGLU).

FeaturePlot(ple5, features="SLC17A7", label=TRUE, pt.size = 0.1)

Clusters 2, 3, 8, 9, 11 and 18 are excitatory glutamatergic neurons.

Cluster 0

cluster0_markers[1:10,0]
## data frame with 0 columns and 10 rows

SHISA8, TSHZ1 and SP9 are olfactory expressed genes according to mouse brain data base. GAD1/2 are expressed in cluster 0. Those cells are inhibitory and have GABA as their primary neurotransmitter (GABAergic).

Clusters 1

cluster1_markers[1:5,0]
## data frame with 0 columns and 5 rows

MDK and GJA1 are expressed in glial cells.

In the original publication, SOX9 is a marker for ependymoglia cells (EG).

VlnPlot(ple5, features=c("SOX9"))

Thus cluster 1 is characterized as ependymoglia cells.

Cluster 2

cluster2_markers[1:5,0]
## data frame with 0 columns and 5 rows

According to the database, HPCAL1 and ADCYAP1.2 are expressed in excitatory neurons in the telencephalon. This also agrees with the SLC17A7 expression in this cluster. Thus cluster 2 is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).

VlnPlot(ple5, features=c("HPCAL1", "ADCYAP1"))

Cluster 3 According to the publication, the amygdala is localized near the telencephalon and some cells from it might have found their way into the samples taken. The amygdala is demarcated by the expression of Slc17a6 and Nr2f2 and the absence of Sox6.

VlnPlot(ple5, features=c("SLC17A6", "NR2F2", "SOX6"))

Cluster 3 appears to match with this description.

Cluster 4

cluster4_markers[1:10,]
##                  p_val avg_log2FC pct.1 pct.2     p_val_adj cluster   gene
## ZIC2     1.265573e-224   2.629953 0.915 0.090 1.658281e-220       4   ZIC2
## PRDM16.1 5.119572e-220   1.842833 0.829 0.065 6.708176e-216       4 PRDM16
## SCN5A    1.288534e-203   1.825381 0.658 0.034 1.688366e-199       4  SCN5A
## ZIC5     1.806208e-185   1.384147 0.658 0.040 2.366674e-181       4   ZIC5
## ZIC4     1.544861e-172   1.537982 0.824 0.091 2.024232e-168       4   ZIC4
## ZIC3     2.368269e-157   1.232324 0.774 0.089 3.103143e-153       4   ZIC3
## PRDM12   1.359533e-153   1.060871 0.407 0.009 1.781396e-149       4 PRDM12
## ZIC1.2   2.773102e-125   2.162296 0.955 0.307 3.633596e-121       4   ZIC1
## TNNI2    3.776805e-122   1.013863 0.472 0.033 4.948748e-118       4  TNNI2
## NXPH1    6.652924e-100   1.252521 0.588 0.080  8.717327e-96       4  NXPH1

According to the database, PRDM16 and NXPH1 are expressed in inhibitory GABAergic neurons in the telencephalon. This also agrees with the GAD1/2 expression in this cluster. Thus cluster 4 is classified as TEGABA cells.

Cluster 5

cluster5_markers[1:5,]
##                  p_val avg_log2FC pct.1 pct.2     p_val_adj cluster   gene
## SHISA8.1 1.118125e-159  2.0463273 0.923 0.124 1.465079e-155       5 SHISA8
## SALL3    6.613116e-132  1.6191852 0.740 0.089 8.665165e-128       5  SALL3
## FRMD7    7.223920e-119  0.5162602 0.284 0.003 9.465503e-115       5  FRMD7
## IL17RB   9.994641e-119  0.6061951 0.308 0.006 1.309598e-114       5 IL17RB
## MEIS3.5  4.648662e-112  1.6514132 0.953 0.217 6.091142e-108       5  MEIS3

According to the database, SHISA8, SALL3 and FRMD7 are expressed in the olfactory bulb. Similarly to cluster 0, they also express GAD1/2. Thus cluster 5 is classified as OBGABA cells.

Clusters 6, 7, 15

Based mainly on the expression of DLX5, GAD1 and GAD2, these clusters are classified as TEGABA.

Cluster 8

cluster8_markers[1:5,]
##                 p_val avg_log2FC pct.1 pct.2     p_val_adj cluster   gene
## LHX9    2.601509e-198  1.3616114 0.728 0.037 3.408757e-194       8   LHX9
## PROX1   1.251825e-141  1.0655459 0.550 0.029 1.640266e-137       8  PROX1
## Trhr2.1 2.648372e-128  1.5108549 0.781 0.094 3.470162e-124       8  Trhr2
## cbln4   7.030047e-128  1.4724907 0.735 0.083 9.211471e-124       8  cbln4
## MGAT4C  9.213471e-122  0.7366753 0.682 0.072 1.207241e-117       8 MGAT4C

According to the database, PROX1 and MGAT4C are expressed in excitatory neurons in the telencephalon. This also agrees with the SLC17A7 expression in this cluster. Thus cluster 8 is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).

Cluster 9

cluster9_markers[1:5,]
##                 p_val avg_log2FC pct.1 pct.2    p_val_adj cluster    gene
## PHF2.5   5.874522e-30 -0.7291210 0.122 0.814 7.697387e-26       9    PHF2
## SETBP1.3 9.812078e-30 -0.7914181 0.157 0.858 1.285677e-25       9  SETBP1
## SMARCE1  9.994600e-30 -0.2648791 0.122 0.826 1.309592e-25       9 SMARCE1
## M6PR.1   1.395972e-29 -0.6659022 0.043 0.671 1.829143e-25       9    M6PR
## EIF4G1.1 1.582265e-29 -0.5498840 0.087 0.758 2.073241e-25       9  EIF4G1

This cluster is mostly defined by the abscence of certain gene transcripts. Based on SLC17A7 expression in this cluster, it is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).

Cluster 10

cluster10_markers[1:5,]
##                  p_val avg_log2FC pct.1 pct.2     p_val_adj cluster   gene
## PDYN     2.229147e-211  3.1016152 0.654 0.017 2.920851e-207      10   PDYN
## TAC1     1.157410e-168  4.0041794 0.776 0.047 1.516554e-164      10   TAC1
## ADRA2B   5.616633e-163  0.7611294 0.458 0.008 7.359474e-159      10 ADRA2B
## ZNF503.1 1.065105e-116  1.0059643 0.794 0.082 1.395606e-112      10 ZNF503
## RASD2.1  9.478990e-113  1.6575809 0.813 0.095 1.242032e-108      10  RASD2

According to the database, PDYN, TAC1 and RASD2.1 are expressed in inhibitory GABAergic neurons in the telencephalon. This also agrees with the GAD1/2 expression in this cluster. Thus cluster 10 is classified as TEGABA cells.

Cluster 11

cluster11_markers[1:10,]
##                    p_val avg_log2FC pct.1 pct.2     p_val_adj cluster     gene
## KCNK3      1.818557e-170  1.1124605 0.758 0.040 2.382855e-166      11    KCNK3
## ARSJ       3.644082e-154  0.7474137 0.667 0.031 4.774841e-150      11     ARSJ
## AMIGO3     4.483239e-148  0.7425097 0.576 0.022 5.874388e-144      11   AMIGO3
## C11orf52.1 3.378173e-130  0.9203419 0.717 0.051 4.426420e-126      11 C11orf52
## NPTX1      5.421971e-127  4.0275194 1.000 0.162 7.104409e-123      11    NPTX1
## TESC.1     2.312956e-123  1.4817564 0.939 0.113 3.030666e-119      11     TESC
## DRAXIN.1   2.254627e-108  1.6689874 0.879 0.116 2.954238e-104      11   DRAXIN
## KANK4.1    8.209235e-102  1.3416315 0.899 0.123  1.075656e-97      11    KANK4
## TBR1.1     4.373672e-100  1.1838994 0.919 0.124  5.730823e-96      11     TBR1
## GPR158.1    4.648132e-97  1.5164614 0.929 0.171  6.090447e-93      11   GPR158

According to the database, ARSJ, NPTX1, TESC.1 and DRAXIN.1 are expressed in excitatory neurons in the telencephalon. This also agrees with the SLC17A7 expression in this cluster. Thus cluster 11 is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).

It is interesting that KCNK3, KANK4.1 and AMIGO3 are also associated with the neural crest in mice…

Cluster 12

cluster12_markers[1:5,]
##        p_val avg_log2FC pct.1 pct.2 p_val_adj cluster   gene
## C1QB       0   4.807704 0.750 0.003         0      12   C1QB
## CSF1R      0   4.228635 0.703 0.003         0      12  CSF1R
## C1QA       0   4.102269 0.719 0.003         0      12   C1QA
## TREM2      0   4.066687 0.781 0.002         0      12  TREM2
## ARPC1B     0   3.918109 0.969 0.013         0      12 ARPC1B
VlnPlot(ple5, features="C1QB")

C1QB is a marker gene for microglia (MG) according to the original publication. Indeed, cluster 12 has by far the highest expression of this marker gene and is defined primarly by it.

Cluster 13

cluster13_markers[1:10,]
##                 p_val avg_log2FC pct.1 pct.2     p_val_adj cluster    gene
## AGT      0.000000e+00   5.415024 0.982 0.011  0.000000e+00      13     AGT
## OLFML3   0.000000e+00   4.814740 0.964 0.010  0.000000e+00      13  OLFML3
## GJB6     0.000000e+00   4.220532 0.875 0.005  0.000000e+00      13    GJB6
## SLC38A4  0.000000e+00   3.745592 0.821 0.003  0.000000e+00      13 SLC38A4
## ATP8B3   0.000000e+00   3.520824 0.839 0.007  0.000000e+00      13  ATP8B3
## LUM      0.000000e+00   3.329207 0.786 0.005  0.000000e+00      13     LUM
## SLC6A13  0.000000e+00   2.425458 0.714 0.002  0.000000e+00      13 SLC6A13
## COL20A1  0.000000e+00   2.374799 0.768 0.001  0.000000e+00      13 COL20A1
## slc35c2  0.000000e+00   1.628434 0.661 0.001  0.000000e+00      13 slc35c2
## SLC13A2 4.559943e-299   2.141380 0.625 0.001 5.974894e-295      13 SLC13A2

GJB6 and SLC6A13 are expressed in vascular cells (VC) according to the database.

In the publication COL1A2 is a marker gene for VCs.

VlnPlot(ple5, features=c("GJB6", "SLC6A13", "COL1A2"))

The above data agree with the hypothesis that Cluster 13 consists of vascular cells.

Cluster 14

cluster14_markers[1:5,]
##                 p_val avg_log2FC pct.1 pct.2     p_val_adj cluster   gene
## LHX6    1.871491e-165  1.0051091 0.821 0.020 2.452215e-161      14   LHX6
## BMP3    3.861901e-116  1.6591241 0.641 0.019 5.060248e-112      14   BMP3
## SHCBP1   2.087033e-81  0.4253901 0.410 0.010  2.734639e-77      14 SHCBP1
## MAF      1.015776e-75  0.6168007 0.487 0.018  1.330971e-71      14    MAF
## PCSK6.2  7.117844e-70  2.0468015 0.974 0.103  9.326511e-66      14  PCSK6

According to the database, LHX6 and MAF are expressed in inhibitory GABAergic neurons in the telencephalon. This also agrees with the GAD1/2 expression in this cluster. Thus cluster 14 is classified as TEGABA cells.

Cluster 16

cluster16_markers[1:5,]
##                p_val avg_log2FC pct.1 pct.2     p_val_adj cluster   gene
## NKX2-2  0.000000e+00   2.461787 0.943 0.004  0.000000e+00      16 NKX2-2
## UGT8    0.000000e+00   2.087565 0.943 0.007  0.000000e+00      16   UGT8
## GJB1   5.308897e-245   1.277924 0.686 0.004 6.956247e-241      16   GJB1
## BCAS1  1.882655e-243   3.245117 0.971 0.015 2.466843e-239      16  BCAS1
## SOX10  3.483498e-225   1.921152 0.943 0.016 4.564428e-221      16  SOX10

According to the database, NKX2-2, UGT8, GJB1, BCAS1 and SOX10 are markers for Oligodentrocytes.

In the original publication, NINJ2 is a marker for Oligodendrocytes (Olig). PDGFRA, GFAP and SOX2 are markers for oligodendrocyte precursor cells (OPC).

VlnPlot(ple5, features=c("NINJ2", "PDGFRA", "GFAP"))

Check if Olig and OPC markers are expressed in the same cells.

FeaturePlot(ple5, features=c("NINJ2", "GFAP"), blend=TRUE, pt.size = 0.1)

As separate cells in cluster 16 express markers for both Oligodentrocytes and oligodendrocyte precursor cells, the cluster seems both to be a mix of the two.

Cluster 17

cluster17_markers[1:20,]
##                 p_val avg_log2FC pct.1 pct.2     p_val_adj cluster    gene
## PLP1     0.000000e+00  3.5394091 1.000 0.007  0.000000e+00      17    PLP1
## FOXD3    0.000000e+00  2.6648292 0.966 0.000  0.000000e+00      17   FOXD3
## EMP1     0.000000e+00  2.4445248 0.931 0.006  0.000000e+00      17    EMP1
## COL23A1 1.103640e-242  4.4754481 1.000 0.014 1.446100e-238      17 COL23A1
## CDH19.1 9.588781e-235  2.3768486 0.897 0.011 1.256418e-230      17   CDH19
## SOX10.1 5.556994e-215  2.5353671 1.000 0.017 7.281329e-211      17   SOX10
## MMP21   1.769177e-211  0.8379563 0.448 0.000 2.318152e-207      17   MMP21
## Hmu     5.953708e-193  0.8252596 0.379 0.000 7.801144e-189      17     Hmu
## TGM2    2.781182e-189  2.1962025 0.690 0.007 3.644182e-185      17    TGM2
## BCAS1.1 2.073947e-181  2.7793688 0.931 0.018 2.717493e-177      17   BCAS1
## SMOC1   1.162292e-162  1.3891680 0.655 0.009 1.522952e-158      17   SMOC1
## MATN2.1 3.111484e-160  1.9157914 0.862 0.019 4.076978e-156      17   MATN2
## SP5     6.211295e-158  1.4750334 0.621 0.008 8.138660e-154      17     SP5
## PRSS56  5.846051e-156  1.5930428 0.483 0.003 7.660081e-152      17  PRSS56
## ABCA9.1 7.317769e-152  4.6154831 1.000 0.031 9.588472e-148      17   ABCA9
## CNDP1   7.948127e-149  1.1587142 0.483 0.004 1.041443e-144      17   CNDP1
## MMP25   2.951980e-146  0.6650071 0.345 0.001 3.867979e-142      17   MMP25
## LAMB1.1 3.386741e-142  1.8714983 0.828 0.020 4.437646e-138      17   LAMB1
## MUSK    1.055070e-140  0.6984520 0.276 0.000 1.382458e-136      17    MUSK
## eva1c   5.648753e-140  1.2631751 0.517 0.006 7.401561e-136      17   eva1c

PLP1 is a marker for glial cells according to the database.

In the publication PRSS56 is the marker gene for olfactory ensheathing cells (OEC), a type of microglia cells.

VlnPlot(ple5, features=c("PRSS56", "PLP1"))

Indeed, PRSS56 is expressed exclusively in cluster 17, marking it as OEC.

Cluster 18

cluster18_markers[1:10,]
##                  p_val avg_log2FC pct.1 pct.2     p_val_adj cluster     gene
## TBX21     0.000000e+00  2.1402989  0.80 0.003  0.000000e+00      18    TBX21
## EOMES    9.073989e-235  2.3627015  0.88 0.009 1.188965e-230      18    EOMES
## TMEM200B 2.568932e-209  1.2979851  0.68 0.005 3.366072e-205      18 TMEM200B
## CDHR1    1.011829e-195  3.1622721  0.96 0.016 1.325799e-191      18    CDHR1
## SYTL4    3.586036e-163  1.1692959  0.68 0.008 4.698783e-159      18    SYTL4
## TFAP2E   3.596488e-163  0.8224352  0.32 0.000 4.712479e-159      18   TFAP2E
## NDNF.1    1.733739e-79  1.9400447  0.68 0.024  2.271718e-75      18     NDNF
## RSPO3     5.957526e-74  0.9755796  0.48 0.012  7.806146e-70      18    RSPO3
## TFAP2C    1.103026e-63  0.7155998  0.44 0.012  1.445295e-59      18   TFAP2C
## RASSF2.1  3.803375e-61  1.0992209  0.72 0.039  4.983563e-57      18   RASSF2

TBX21 is a marker gene for neuroblasts according to the mouse database. In the publication, immature neurons are characterized by the expression of SOX4.

VlnPlot(ple5, features=c("SOX4", "TBX21"))

SOX4 has the highest expression in cluster 18.

Thus, cells in cluster 18 are immature neurons (ImN).

ple5 <- SetIdent(ple5,value = "RNA_snn_res.0.5")
ple5 <- RenameIdents(object = ple5,
                     "0" = "OBGABA",
                     "1" = "EG",
                     "2" = "TEGLUT",
                     "3" = "nonTE.GLUT",
                     "4" = "TEGABA",
                     "5" = "OBGABA",
                     "6" = "TEGABA",
                     "7" = "TEGABA",
                     "8" = "TEGLUT",
                     "9" = "TEGLUT",
                     "10" = "TEGABA",
                     "11" = "TEGLUT",
                     "12" = "MG",
                     "13" = "VC",
                     "14" = "TEGABA",
                     "15" = "TEGABA",
                     "16" = "Olig/OPC",
                     "17" = "OEC",
                     "18" = "ImN"
)

DimPlot(ple5, label=TRUE)

Create metadata column for broad cell types

Now we will save the information about broad cell type as a column in the metadata.

ple5$broad.cell.types <- as.character(Idents(ple5))
head(ple5[[]])
##                   orig.ident nCount_RNA nFeature_RNA percent_mito
## TCAGCCTGTTTACGTG Pleurodeles      29999         5725    0.6266876
## CAACAGTGTTAGAAGT Pleurodeles      29764         5733    1.8411504
## CATGCGGGTCCTACAA Pleurodeles      29703         5834    1.1210989
## AAACCCATCCTTCTTC Pleurodeles      29647         5733    0.4081357
## CCTCACAAGATGGGCT Pleurodeles      29645         5801    1.0895598
## GAAGGGTAGATAGCTA Pleurodeles      29640         5734    0.3609987
##                  RNA_snn_res.0.1 RNA_snn_res.0.5 RNA_snn_res.0.7 RNA_snn_res.1
## TCAGCCTGTTTACGTG               5              10              10            11
## CAACAGTGTTAGAAGT               3               8               8             7
## CATGCGGGTCCTACAA               3               2               1             0
## AAACCCATCCTTCTTC               3               2               1             0
## CCTCACAAGATGGGCT               0               6               5             3
## GAAGGGTAGATAGCTA               3               8               8             7
##                  RNA_snn_res.1.5 RNA_snn_res.2 RNA_snn_res.3 seurat_clusters
## TCAGCCTGTTTACGTG              10             9             7               7
## CAACAGTGTTAGAAGT               4             2            17              17
## CATGCGGGTCCTACAA               6            18            19              19
## AAACCCATCCTTCTTC              13            11            10              10
## CCTCACAAGATGGGCT               1            21            20              20
## GAAGGGTAGATAGCTA               4             2            11              11
##                  broad.cell.types
## TCAGCCTGTTTACGTG           TEGABA
## CAACAGTGTTAGAAGT           TEGLUT
## CATGCGGGTCCTACAA           TEGLUT
## AAACCCATCCTTCTTC           TEGLUT
## CCTCACAAGATGGGCT           TEGABA
## GAAGGGTAGATAGCTA           TEGLUT

Visualize the number of cell counts per cell type

data <- as.data.frame(table(ple5$broad.cell.types))
colnames(data) <- c("cell.type","count")

ncells <- ggplot(data, aes(x = cell.type, y = count, fill = cell.type)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = count), 
            position=position_dodge(width=0.9), 
            vjust=-0.25) +
  ggtitle("Cells per cell type") +
  theme(legend.position =  "none") + 
  theme(axis.text.x = element_text(angle = 45, hjust=1))
ncells

TEGABA and TEGLUT have the largest number of counts (cells), while Immature Neurons and Olfactory Ensheathing Cells are the fewest.

Make a DotPlot to visualize gene expression across clusters

Let’s make a dotplot as another way to compare expression levels between clusters. Let’s try to recreate Fig.1D from the Woych et al (2022) paper.

Major_Features <- c("SNAP25","SYT1","RBFOX3","FOXG1","SLC17A7","GAD1",
                    "SLC17A6","SOX4","SOX9","SOX2","GFAP","PDGFRA",
                    "NINJ2","COL1A2","PRSS56","C1QB","LCP1")

ple5 <- SetIdent(ple5,value = "broad.cell.types")
DotPlot(ple5, features=Major_Features)

Now let’s reorder the clusters so it looks even more like their figure.

head(Idents(ple5))
## TCAGCCTGTTTACGTG CAACAGTGTTAGAAGT CATGCGGGTCCTACAA AAACCCATCCTTCTTC 
##           TEGABA           TEGLUT           TEGLUT           TEGLUT 
## CCTCACAAGATGGGCT GAAGGGTAGATAGCTA 
##           TEGABA           TEGLUT 
## Levels: TEGABA TEGLUT ImN nonTE.GLUT OBGABA Olig/OPC EG VC MG OEC
Idents(ple5) <- factor(Idents(ple5), 
                            levels=c("MG","OEC","VC","Olig/OPC",
                                   "EG","ImN",
                                   "OBGABA", "nonTE.GLUT", "TEGABA","TEGLUT"))
DotPlot(ple5, features=Major_Features) + 
  theme(axis.text.x = element_text(angle = 45, vjust=0.9, hjust=0.9))

The graph looks very similar to the original publication. SNAP25, SYT1, RBFOX3 and FOXG1, although not used for cluster annotation, agree well with the published figure.

We can alternatively plot gene expression using heatmaps. Here each column represents a cell and each row is a gene.

DoHeatmap(subset(ple5, downsample = 50), features = Major_Features, size = 3)
## Warning in DoHeatmap(subset(ple5, downsample = 50), features = Major_Features,
## : The following features were omitted as they were not found in the scale.data
## slot for the RNA assay: FOXG1, RBFOX3

Save the Seurat object with the broad annotation for day 5

saveRDS(ple5, "srt_04_ple5v2.rds")
sessionInfo()
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Berlin
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] dplyr_1.1.2        clustree_0.5.0     ggraph_2.1.0       ggplot2_3.4.2     
## [5] SeuratObject_4.1.3 Seurat_4.3.0      
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3     rstudioapi_0.14        jsonlite_1.8.4        
##   [4] magrittr_2.0.3         spatstat.utils_3.0-3   farver_2.1.1          
##   [7] rmarkdown_2.21         vctrs_0.6.2            ROCR_1.0-11           
##  [10] spatstat.explore_3.1-0 htmltools_0.5.5        sass_0.4.6            
##  [13] sctransform_0.3.5      parallelly_1.35.0      KernSmooth_2.23-21    
##  [16] bslib_0.4.2            htmlwidgets_1.6.2      ica_1.0-3             
##  [19] plyr_1.8.8             plotly_4.10.1          zoo_1.8-12            
##  [22] cachem_1.0.8           igraph_1.4.2           mime_0.12             
##  [25] lifecycle_1.0.3        pkgconfig_2.0.3        Matrix_1.5-4          
##  [28] R6_2.5.1               fastmap_1.1.1          fitdistrplus_1.1-11   
##  [31] future_1.32.0          shiny_1.7.4            digest_0.6.31         
##  [34] colorspace_2.1-0       patchwork_1.1.2        tensor_1.5            
##  [37] irlba_2.3.5.1          labeling_0.4.2         progressr_0.13.0      
##  [40] fansi_1.0.4            spatstat.sparse_3.0-1  httr_1.4.6            
##  [43] polyclip_1.10-4        abind_1.4-5            compiler_4.3.0        
##  [46] withr_2.5.0            viridis_0.6.3          highr_0.10            
##  [49] ggforce_0.4.1          MASS_7.3-60            tools_4.3.0           
##  [52] lmtest_0.9-40          httpuv_1.6.10          future.apply_1.10.0   
##  [55] goftest_1.2-3          glue_1.6.2             nlme_3.1-162          
##  [58] promises_1.2.0.1       grid_4.3.0             Rtsne_0.16            
##  [61] cluster_2.1.4          reshape2_1.4.4         generics_0.1.3        
##  [64] gtable_0.3.3           spatstat.data_3.0-1    tidyr_1.3.0           
##  [67] data.table_1.14.8      tidygraph_1.2.3        sp_1.6-0              
##  [70] utf8_1.2.3             spatstat.geom_3.2-1    RcppAnnoy_0.0.20      
##  [73] ggrepel_0.9.3          RANN_2.6.1             pillar_1.9.0          
##  [76] stringr_1.5.0          later_1.3.1            splines_4.3.0         
##  [79] tweenr_2.0.2           lattice_0.21-8         survival_3.5-5        
##  [82] deldir_1.0-6           tidyselect_1.2.0       miniUI_0.1.1.1        
##  [85] pbapply_1.7-0          knitr_1.42             gridExtra_2.3         
##  [88] scattermore_1.0        xfun_0.39              graphlayouts_1.0.0    
##  [91] matrixStats_0.63.0     stringi_1.7.12         lazyeval_0.2.2        
##  [94] yaml_2.3.7             evaluate_0.21          codetools_0.2-19      
##  [97] tibble_3.2.1           cli_3.6.1              uwot_0.1.14           
## [100] xtable_1.8-4           reticulate_1.28        munsell_0.5.0         
## [103] jquerylib_0.1.4        Rcpp_1.0.10            globals_0.16.2        
## [106] spatstat.random_3.1-4  png_0.1-8              parallel_4.3.0        
## [109] ellipsis_0.3.2         listenv_0.9.0          viridisLite_0.4.2     
## [112] scales_1.2.1           ggridges_0.5.4         leiden_0.4.3          
## [115] purrr_1.0.1            rlang_1.1.1            cowplot_1.1.1